rm(list =ls())library(mlbench)
library(caret)## Loading required package: ggplot2## Loading required package: lattice#install.packages("caTools")
library(caTools)
#install.packages("ranger")
library(ranger)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#install.packages("doParallel")
library(doParallel)## Loading required package: foreach## Loading required package: iterators## Loading required package: parallel#load the training features
training_features <- read.csv("training_set_features.csv", row.names = 1, header = T, stringsAsFactors = T)
#View(training_features)#load the validation labels
training_labels <- read.csv("training_set_labels.csv", row.names = 1, header = T, stringsAsFactors = T)
View(training_labels)#combining labels
h1n1 <- cbind(training_features, training_labels[1:2])View(h1n1)#sum of all NA values in each dataset
colSums(is.na(h1n1))## h1n1_concern h1n1_knowledge
## 92 116
## behavioral_antiviral_meds behavioral_avoidance
## 71 208
## behavioral_face_mask behavioral_wash_hands
## 19 42
## behavioral_large_gatherings behavioral_outside_home
## 87 82
## behavioral_touch_face doctor_recc_h1n1
## 128 2160
## doctor_recc_seasonal chronic_med_condition
## 2160 971
## child_under_6_months health_worker
## 820 804
## health_insurance opinion_h1n1_vacc_effective
## 12274 391
## opinion_h1n1_risk opinion_h1n1_sick_from_vacc
## 388 395
## opinion_seas_vacc_effective opinion_seas_risk
## 462 514
## opinion_seas_sick_from_vacc age_group
## 537 0
## education race
## 0 0
## sex income_poverty
## 0 0
## marital_status rent_or_own
## 0 0
## employment_status hhs_geo_region
## 0 0
## census_msa household_adults
## 0 249
## household_children employment_industry
## 249 0
## employment_occupation h1n1_vaccine
## 0 0
## seasonal_vaccine
## 0health insurance has the largest NA values 12274. Filling up thr data might cause inaccuracies so we will drop the column “health_insurance”.
#removing "health_insurance" column from the dataset
h1n1$health_insurance <- NULL
#also dropping "hhs_goe_region", "employment_industry" and "employment_occupation"
h1n1$hhs_geo_region <- NULL
h1n1$employment_industry <- NULL
h1n1$employment_occupation <- NULL
#View(h1n1)#dropping all NA values in the dataset
h1n1 <- na.omit(h1n1)
#checking the dimension of NA after dropping all the values
dim(h1n1)## [1] 22976 33From 26707 entries, we now have a total of 22976(a difference of 3731).
#duplicating dataset
h1n1_2 <- h1n1
h1n1_2[,32:33] <- NULL
#View(h1n1_2)#binarising the nominal attributes
binary_data <- dummyVars(~., data = h1n1_2)
#View(binary_data)
#adding the conversion to the data
new_data <- predict(binary_data, newdata = h1n1_2)#adding "h1n1_vaccine class" back to original dataset
new_data2 <- cbind(new_data, h1n1[32])View(new_data2)#converting 0 and 1 to "yes" and "no" for the decision class
new_data2$h1n1_vaccine <- factor(new_data2$h1n1_vaccine, levels = c(0, 1), labels = c("no", "yes"))
new_data2## h1n1_concern h1n1_knowledge behavioral_antiviral_meds behavioral_avoidance
## 0 1 0 0 0
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## 2009 1 3 1
## 2010 0 1 1
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## 2012 0 5 2
## 2013 0 2 1
## 2014 0 5 4
## 2015 0 4 2
## 2016 0 4 2
## 2017 0 5 5
## 2019 0 4 2
## 2020 0 3 2
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## opinion_h1n1_sick_from_vacc opinion_seas_vacc_effective opinion_seas_risk
## 0 2 2 1
## 1 4 4 2
## 3 5 5 4
## 4 2 3 1
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## 6 1 4 2
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## 1992 1 0
## 1993 0 1
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## 1998 0 1
## 1999 0 1
## 2002 0 0
## 2003 0 0
## 2004 0 1
## 2005 1 0
## 2006 0 1
## 2007 0 1
## 2008 1 0
## 2009 1 0
## 2010 0 1
## 2011 0 1
## 2012 0 1
## 2013 1 0
## 2014 0 0
## 2015 0 1
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## 2017 1 0
## 2019 1 0
## 2020 1 0
## 2021 1 0
## 2022 1 0
## 2023 0 1
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## 2073 1 0
## income_poverty.Below Poverty marital_status. marital_status.Married
## 0 1 0 0
## 1 1 0 0
## 3 1 0 0
## 4 0 0 1
## 5 0 0 1
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## 30 0 0 0
## 31 0 0 0
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## 39 0 0 0
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## 100 0 0 0
## 101 0 0 1
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## 185 0 0 1
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## 194 0 0 1
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## 196 0 0 1
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## 199 0 0 0
## 200 0 0 0
## 201 0 0 0
## 202 0 0 0
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## 212 0 0 1
## 213 0 0 0
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## 221 0 0 0
## 222 0 0 0
## 223 1 0 1
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## 1974 0 1
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## 1978 0 0
## 1979 0 0
## 1980 0 0
## 1982 1 0
## 1983 0 1
## 1987 0 0
## 1989 1 0
## 1990 1 0
## 1991 0 0
## 1992 0 1
## 1993 0 1
## 1994 0 0
## 1996 1 0
## 1997 1 0
## 1998 1 0
## 1999 0 1
## 2002 0 0
## 2003 0 1
## 2004 1 0
## 2005 0 0
## 2006 1 0
## 2007 1 0
## 2008 1 0
## 2009 1 0
## 2010 0 1
## 2011 1 0
## 2012 0 1
## 2013 0 0
## 2014 1 0
## 2015 1 0
## 2016 1 0
## 2017 1 0
## 2019 0 1
## 2020 1 0
## 2021 0 0
## 2022 1 0
## 2023 1 0
## 2024 0 1
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## 2027 0 1
## 2028 0 0
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## 2030 1 0
## 2031 0 1
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## 2040 1 0
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## 2045 1 0
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## 2069 1 0
## 2070 0 1
## 2071 0 1
## 2072 0 1
## 2073 0 1
## census_msa.Non-MSA household_adults household_children h1n1_vaccine
## 0 1 0 0 no
## 1 0 0 0 no
## 3 0 0 0 no
## 4 0 1 0 no
## 5 0 2 3 no
## 6 0 0 0 no
## 7 1 2 0 yes
## 8 0 1 0 no
## 9 0 0 0 no
## 10 0 2 0 yes
## 11 1 1 2 yes
## 12 0 1 0 no
## 13 0 1 2 no
## 14 0 1 0 no
## 15 0 1 3 no
## 16 0 0 0 yes
## 17 0 1 0 no
## 18 1 2 0 no
## 19 0 1 0 no
## 20 0 0 0 no
## 21 0 1 0 no
## 22 0 2 1 no
## 23 1 1 2 no
## 24 1 2 0 no
## 25 0 1 1 no
## 26 0 1 2 yes
## 27 1 1 0 no
## 28 1 1 0 no
## 29 0 0 0 no
## 30 0 0 2 no
## 31 0 3 0 yes
## 32 0 1 2 no
## 33 0 0 0 yes
## 34 0 1 2 yes
## 35 0 0 0 yes
## 36 1 2 0 no
## 37 0 0 0 no
## 38 0 1 0 yes
## 39 0 0 0 no
## 40 0 0 0 no
## 41 0 1 0 yes
## 43 0 0 1 yes
## 45 0 2 1 no
## 46 0 1 0 yes
## 47 0 1 0 yes
## 48 1 1 0 yes
## 49 0 1 3 yes
## 50 1 1 2 no
## 51 0 0 0 no
## 52 1 1 0 no
## 53 0 0 0 no
## 54 0 0 1 no
## 55 0 1 0 no
## 56 1 0 0 no
## 58 0 1 2 no
## 59 0 1 2 no
## 60 0 0 0 yes
## 61 0 1 2 no
## 62 0 2 0 no
## 63 0 3 1 no
## 65 0 0 0 no
## 66 1 1 2 no
## 67 0 0 1 yes
## 68 1 1 0 no
## 69 0 0 0 no
## 70 0 2 1 no
## 71 0 0 0 no
## 72 0 0 0 no
## 73 0 0 0 no
## 74 1 1 0 yes
## 75 0 1 2 yes
## 77 0 1 2 no
## 78 0 0 0 no
## 79 1 1 0 no
## 80 1 0 0 no
## 81 0 1 0 no
## 82 0 1 1 yes
## 83 0 0 1 no
## 84 1 1 0 no
## 85 0 1 0 yes
## 86 0 2 1 no
## 87 0 1 1 no
## 88 1 0 0 no
## 91 1 1 2 no
## 92 0 0 0 no
## 93 0 1 2 no
## 95 0 2 0 no
## 98 0 3 3 no
## 99 0 1 0 no
## 100 0 1 0 no
## 101 1 1 0 no
## 103 1 1 1 no
## 104 0 0 0 no
## 106 0 2 0 no
## 107 1 1 2 yes
## 108 0 1 2 no
## 109 0 3 3 no
## 110 1 1 0 yes
## 111 0 1 0 no
## 112 0 2 0 yes
## 113 0 2 0 no
## 114 0 1 2 no
## 115 0 0 0 no
## 116 1 1 0 yes
## 117 0 1 0 no
## 118 0 1 0 no
## 120 1 1 0 no
## 121 0 0 0 yes
## 122 0 0 0 yes
## 123 0 1 0 no
## 124 0 1 0 no
## 125 0 1 3 no
## 126 0 1 0 no
## 127 0 3 0 yes
## 128 1 0 0 yes
## 129 0 0 0 no
## 130 1 1 0 no
## 131 0 0 0 no
## 132 0 1 2 no
## 133 0 0 0 no
## 134 0 0 0 no
## 135 0 0 0 no
## 137 0 1 0 no
## 138 0 1 0 no
## 139 0 1 3 no
## 140 0 1 0 yes
## 141 1 1 0 no
## 142 0 0 0 no
## 143 0 0 0 no
## 144 1 0 1 no
## 145 0 0 0 no
## 146 0 2 2 no
## 147 0 0 0 no
## 148 1 1 0 no
## 151 0 0 0 yes
## 152 1 1 1 no
## 154 1 0 0 no
## 155 0 1 2 no
## 156 0 1 0 no
## 157 1 0 0 no
## 158 1 2 0 yes
## 159 0 1 0 yes
## 160 1 1 0 no
## 161 0 1 2 no
## 162 0 1 3 yes
## 163 0 1 3 no
## 164 0 0 0 no
## 165 0 1 2 no
## 166 1 1 2 no
## 167 0 1 0 yes
## 168 1 1 0 yes
## 170 1 2 3 no
## 171 1 0 3 no
## 172 0 1 0 yes
## 173 1 2 0 no
## 174 0 3 1 no
## 176 1 0 0 no
## 177 0 0 0 no
## 178 0 1 1 yes
## 179 0 1 3 yes
## 181 0 1 0 no
## 182 0 1 0 no
## 185 1 1 0 yes
## 186 0 0 0 no
## 187 0 0 0 no
## 188 1 2 0 no
## 189 0 1 0 no
## 190 1 0 3 no
## 191 0 1 1 no
## 192 0 3 0 yes
## 193 0 1 0 no
## 194 1 1 0 no
## 195 1 1 0 yes
## 196 0 1 0 yes
## 197 1 2 2 no
## 199 0 2 0 no
## 200 0 1 1 no
## 201 1 1 0 no
## 202 0 1 0 no
## 204 0 3 0 yes
## 206 1 1 3 no
## 207 0 0 0 no
## 208 0 1 0 no
## 209 1 1 0 no
## 210 0 0 2 no
## 211 0 1 3 yes
## 212 0 1 0 no
## 213 0 2 0 yes
## 214 1 1 0 no
## 215 0 1 0 yes
## 216 0 1 0 no
## 218 0 0 0 no
## 219 1 1 2 yes
## 220 0 1 0 no
## 221 0 1 0 no
## 222 0 1 0 no
## 223 0 1 0 no
## 224 0 1 0 no
## 225 0 1 0 yes
## 226 0 0 0 no
## 227 0 2 0 no
## 228 0 1 1 yes
## 229 0 1 0 no
## 230 0 1 0 no
## 231 1 1 0 no
## 232 1 0 0 no
## 233 0 1 1 no
## 235 0 1 0 no
## 236 1 1 0 no
## 240 0 0 0 no
## 241 0 1 0 no
## 242 1 0 3 no
## 243 1 0 0 no
## 244 0 3 0 no
## 245 0 0 0 no
## 246 0 1 3 no
## 247 0 1 1 no
## 248 0 1 2 no
## 249 0 2 0 no
## 250 0 1 0 yes
## 251 0 1 0 no
## 252 0 0 0 no
## 253 0 1 0 no
## 254 0 1 0 no
## 255 0 3 3 no
## 256 0 1 0 no
## 257 0 1 0 no
## 258 0 2 0 no
## 259 1 2 2 no
## 260 0 1 0 no
## 261 0 0 0 no
## 262 1 1 0 yes
## 263 1 1 0 yes
## 264 1 1 0 no
## 265 0 0 0 no
## 266 0 1 2 no
## 267 0 1 0 no
## 268 0 1 2 yes
## 270 0 1 3 no
## 271 0 1 3 no
## 272 0 2 0 no
## 273 1 0 0 no
## 274 0 2 0 no
## 275 0 2 0 no
## 276 0 1 0 yes
## 277 0 2 1 no
## 278 1 1 2 yes
## 279 0 0 2 no
## 281 1 1 1 no
## 282 0 0 0 no
## 283 0 3 0 no
## 284 1 0 2 yes
## 286 0 1 0 no
## 287 0 1 1 no
## 288 0 1 0 no
## 289 0 0 0 no
## 290 0 1 0 no
## 291 1 0 0 no
## 292 0 1 2 no
## 293 1 0 0 no
## 294 0 1 3 yes
## 295 0 0 0 no
## 296 1 1 0 no
## 297 1 2 0 yes
## 298 0 3 0 no
## 299 0 0 0 no
## 300 0 0 0 no
## 301 0 1 0 no
## 302 0 0 0 no
## 303 0 1 0 yes
## 304 1 1 0 no
## 305 1 0 0 no
## 306 1 3 1 yes
## 307 1 1 1 yes
## 308 0 3 0 yes
## 309 0 0 0 no
## 310 0 0 0 no
## 311 1 2 0 no
## 312 0 1 0 no
## 313 1 0 0 no
## 314 0 1 0 no
## 315 0 2 1 no
## 316 0 2 2 no
## 317 0 1 2 no
## 318 1 1 0 yes
## 319 0 3 0 yes
## 320 0 0 0 no
## 322 0 1 0 yes
## 323 0 1 0 yes
## 325 0 1 2 yes
## 326 0 1 2 no
## 327 0 1 0 no
## 329 0 1 0 yes
## 330 1 0 0 no
## 331 0 0 0 no
## 333 0 2 0 no
## 335 1 1 0 no
## 336 0 0 0 no
## 337 1 1 0 no
## 338 1 0 0 no
## 339 0 1 2 yes
## 340 1 1 1 no
## 341 0 0 0 no
## 342 0 1 0 no
## 343 0 2 1 no
## 344 0 0 1 no
## 346 1 3 1 yes
## 348 0 1 0 yes
## 349 0 1 0 no
## 350 0 1 0 no
## 351 0 1 0 no
## 353 0 0 2 no
## 354 0 1 2 yes
## 355 1 1 2 no
## 356 0 0 3 no
## 357 0 1 0 yes
## 358 1 1 3 no
## 359 1 0 0 yes
## 360 0 3 0 no
## 361 0 0 0 no
## 362 0 0 0 no
## 363 0 2 0 no
## 364 0 0 0 yes
## 365 1 0 0 no
## 366 0 1 0 no
## 367 1 1 0 no
## 368 0 1 1 no
## 369 0 1 0 no
## 370 0 0 0 no
## 371 0 0 0 no
## 372 0 0 0 no
## 374 0 0 2 yes
## 375 0 0 0 yes
## 376 1 1 0 no
## 377 0 1 0 no
## 378 0 1 0 no
## 380 0 1 1 no
## 382 1 1 0 yes
## 384 0 0 0 no
## 385 1 0 0 no
## 387 1 1 1 yes
## 388 0 0 0 yes
## 389 0 0 0 yes
## 390 0 1 2 no
## 391 0 2 0 no
## 393 0 1 0 no
## 394 0 0 0 no
## 395 1 0 0 no
## 396 1 1 2 no
## 397 0 0 0 yes
## 398 1 1 2 no
## 399 0 1 0 yes
## 400 1 1 0 no
## 401 0 0 1 no
## 402 0 1 0 no
## 403 1 0 0 no
## 404 0 1 0 yes
## 407 0 0 0 no
## 408 0 0 0 no
## 412 0 2 0 no
## 413 1 1 0 no
## 414 1 1 1 yes
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## 1513 1 1 1 no
## 1514 0 1 3 no
## 1515 0 1 0 no
## 1517 1 1 3 no
## 1518 0 0 0 no
## 1519 0 1 2 no
## 1520 0 3 1 yes
## 1521 0 2 0 no
## 1522 1 0 1 no
## 1523 1 0 0 no
## 1524 0 1 0 yes
## 1525 0 1 0 no
## 1526 0 2 3 no
## 1527 1 0 0 no
## 1528 0 1 0 no
## 1529 0 1 2 yes
## 1530 0 0 0 no
## 1531 1 1 0 yes
## 1532 1 2 0 yes
## 1533 1 0 0 no
## 1534 1 1 0 no
## 1535 0 0 0 no
## 1536 0 1 1 no
## 1537 0 1 0 no
## 1538 0 0 0 no
## 1539 0 1 2 no
## 1540 0 1 0 no
## 1541 1 0 2 no
## 1542 0 1 0 yes
## 1544 1 0 0 no
## 1545 0 0 2 no
## 1546 0 2 0 no
## 1547 0 1 0 no
## 1548 0 1 2 no
## 1549 0 1 0 no
## 1550 0 1 1 no
## 1551 0 1 0 no
## 1552 0 1 0 no
## 1553 1 1 0 no
## 1554 0 1 0 no
## 1555 1 1 0 no
## 1556 0 2 0 no
## 1557 0 0 0 no
## 1558 1 1 2 no
## 1559 0 0 0 yes
## 1560 1 1 0 no
## 1562 0 2 0 no
## 1563 1 1 1 yes
## 1565 0 1 0 yes
## 1566 1 0 0 no
## 1567 0 1 0 yes
## 1568 0 2 0 yes
## 1569 0 0 0 yes
## 1570 0 0 2 yes
## 1571 0 1 3 no
## 1572 0 1 0 no
## 1574 1 0 0 yes
## 1575 0 2 0 no
## 1576 0 0 0 no
## 1578 0 1 0 no
## 1579 1 1 1 no
## 1580 0 0 0 no
## 1581 0 1 2 no
## 1582 0 0 0 no
## 1583 0 0 0 no
## 1584 0 1 0 no
## 1585 1 1 0 no
## 1586 1 1 3 no
## 1587 1 1 2 no
## 1588 1 1 0 no
## 1589 0 1 2 no
## 1590 1 0 0 yes
## 1591 1 1 0 yes
## 1592 0 2 2 no
## 1593 0 1 0 no
## 1594 0 1 0 no
## 1595 0 1 0 no
## 1596 1 1 0 no
## 1597 0 0 0 no
## 1599 0 0 0 yes
## 1600 0 1 1 yes
## 1601 0 0 0 no
## 1604 0 0 0 no
## 1605 0 0 0 yes
## 1607 0 3 1 no
## 1608 0 0 0 no
## 1609 1 2 0 yes
## 1610 0 1 2 yes
## 1611 0 1 0 no
## 1612 0 1 0 no
## 1613 0 1 0 no
## 1614 0 1 1 no
## 1615 1 1 0 no
## 1616 0 1 0 no
## 1617 1 1 0 no
## 1618 0 0 0 no
## 1619 0 1 0 no
## 1620 0 1 1 yes
## 1622 0 0 1 no
## 1624 1 0 0 yes
## 1625 0 2 0 yes
## 1626 0 1 0 yes
## 1627 0 0 2 no
## 1628 1 0 0 yes
## 1629 0 2 2 no
## 1631 1 2 3 yes
## 1632 0 1 3 no
## 1633 1 1 0 no
## 1634 0 1 0 no
## 1635 0 1 0 yes
## 1638 1 1 1 no
## 1639 0 1 1 no
## 1640 0 0 0 no
## 1641 0 0 0 no
## 1642 0 2 3 no
## 1643 0 3 1 no
## 1644 0 0 0 no
## 1645 0 1 0 no
## 1648 0 0 0 yes
## 1649 0 1 0 no
## 1650 0 1 0 yes
## 1651 0 1 0 no
## 1652 0 1 0 no
## 1654 0 0 0 no
## 1655 0 1 2 no
## 1656 0 1 0 no
## 1657 1 0 0 no
## 1658 0 2 0 no
## 1659 0 1 3 no
## 1660 1 1 0 no
## 1661 0 0 0 no
## 1662 0 1 2 no
## 1663 0 0 0 yes
## 1664 0 1 2 yes
## 1665 0 1 1 no
## 1666 1 0 0 no
## 1667 0 1 3 no
## 1668 0 1 0 no
## 1669 1 0 0 no
## 1670 1 1 0 no
## 1671 1 0 0 no
## 1673 0 0 0 no
## 1674 0 0 0 yes
## 1675 0 0 0 no
## 1676 0 0 0 no
## 1677 0 1 2 no
## 1679 0 1 0 no
## 1680 0 1 0 no
## 1681 0 0 0 yes
## 1682 0 0 2 no
## 1683 0 1 0 no
## 1684 0 2 2 no
## 1685 0 3 0 yes
## 1686 0 1 0 no
## 1687 1 0 0 yes
## 1688 1 0 0 no
## 1689 1 1 0 no
## 1690 0 0 0 yes
## 1691 0 1 2 no
## 1692 0 0 0 no
## 1693 0 2 0 no
## 1694 1 0 0 no
## 1695 0 0 0 no
## 1696 0 1 2 yes
## 1697 0 2 0 yes
## 1698 1 1 0 no
## 1699 0 1 2 no
## 1700 0 1 0 no
## 1702 0 1 0 no
## 1704 1 1 2 no
## 1705 0 1 1 no
## 1707 0 2 0 yes
## 1708 0 1 0 no
## 1709 0 0 0 no
## 1710 0 0 0 no
## 1711 0 1 2 yes
## 1712 1 0 0 no
## 1713 0 0 0 no
## 1714 0 0 0 no
## 1715 0 1 0 no
## 1717 0 1 3 no
## 1718 0 3 3 yes
## 1720 0 1 1 yes
## 1721 0 0 0 no
## 1722 0 2 0 no
## 1723 0 1 1 no
## 1724 1 1 2 yes
## 1725 0 1 0 no
## 1728 0 2 0 no
## 1729 0 1 0 no
## 1730 0 1 0 no
## 1731 0 0 0 no
## 1732 0 1 3 no
## 1733 0 1 0 no
## 1734 0 1 0 no
## 1735 1 0 0 no
## 1736 0 1 0 no
## 1737 0 2 0 no
## 1738 0 0 2 yes
## 1740 0 0 0 no
## 1741 1 1 0 no
## 1742 0 1 0 no
## 1743 1 0 1 no
## 1744 0 1 0 no
## 1745 1 0 0 no
## 1746 1 1 0 yes
## 1747 0 2 1 yes
## 1748 1 0 0 no
## 1749 1 1 0 no
## 1750 0 1 0 no
## 1751 0 0 0 no
## 1752 1 1 2 no
## 1753 0 3 0 no
## 1755 0 1 0 no
## 1756 0 1 0 no
## 1757 0 1 0 no
## 1758 1 0 0 yes
## 1759 1 0 0 no
## 1760 1 0 0 no
## 1761 1 1 3 no
## 1763 1 1 0 no
## 1764 0 1 0 yes
## 1765 0 1 3 yes
## 1766 0 3 3 no
## 1767 0 1 0 yes
## 1768 0 0 0 yes
## 1769 0 1 2 no
## 1770 0 2 3 no
## 1771 0 1 2 no
## 1773 1 0 0 no
## 1776 0 1 0 no
## 1777 0 1 0 no
## 1778 0 0 0 no
## 1779 0 1 3 yes
## 1780 0 1 0 no
## 1781 1 0 0 no
## 1782 0 1 0 no
## 1783 0 2 2 no
## 1784 0 2 0 no
## 1785 1 1 2 yes
## 1786 0 1 1 no
## 1787 0 1 0 yes
## 1788 0 0 2 no
## 1789 0 2 0 no
## 1790 0 0 0 yes
## 1791 0 0 0 yes
## 1792 0 0 0 no
## 1793 0 1 0 no
## 1794 0 0 1 no
## 1797 0 2 0 yes
## 1798 0 1 2 yes
## 1799 0 0 0 no
## 1800 1 1 0 yes
## 1801 1 1 0 yes
## 1802 0 2 2 yes
## 1803 0 0 0 no
## 1804 1 0 0 yes
## 1805 1 2 2 no
## 1807 1 0 0 no
## 1808 1 0 2 yes
## 1809 0 0 0 no
## 1810 0 0 0 no
## 1811 1 1 3 no
## 1813 0 1 0 no
## 1814 0 0 2 yes
## 1815 0 1 0 yes
## 1816 1 1 0 no
## 1817 0 1 0 yes
## 1818 0 1 2 yes
## 1819 0 1 0 no
## 1820 0 2 0 no
## 1821 1 2 0 yes
## 1822 1 0 0 yes
## 1824 1 1 0 no
## 1825 0 2 0 no
## 1826 0 1 1 yes
## 1827 1 1 2 no
## 1828 1 3 1 no
## 1829 1 1 0 yes
## 1830 1 1 0 no
## 1832 0 1 1 no
## 1833 0 0 0 no
## 1834 0 1 2 no
## 1835 0 0 0 no
## 1836 1 2 1 no
## 1837 0 1 2 no
## 1839 0 1 0 no
## 1840 1 2 2 no
## 1841 1 0 0 no
## 1842 0 1 0 yes
## 1844 0 0 0 no
## 1845 0 0 0 yes
## 1846 0 1 0 no
## 1848 0 0 0 yes
## 1849 0 2 0 yes
## 1850 0 1 0 no
## 1853 0 0 0 yes
## 1854 0 1 0 no
## 1855 0 1 1 no
## 1856 0 0 0 no
## 1857 1 2 0 no
## 1859 0 1 3 no
## 1861 0 0 0 yes
## 1863 0 1 1 no
## 1864 1 1 0 yes
## 1865 0 0 0 no
## 1866 0 1 0 no
## 1867 0 1 0 no
## 1868 1 1 0 yes
## 1869 0 1 0 no
## 1870 1 0 0 no
## 1871 1 0 0 no
## 1872 0 0 1 no
## 1873 0 1 0 yes
## 1874 0 0 0 no
## 1875 0 1 0 no
## 1876 0 0 0 yes
## 1877 1 0 0 no
## 1878 0 0 0 no
## 1879 0 1 0 no
## 1880 0 0 0 no
## 1881 0 1 1 no
## 1882 1 1 0 no
## 1883 0 0 0 no
## 1884 0 0 0 no
## 1885 0 2 0 no
## 1886 1 1 0 yes
## 1887 0 0 0 no
## 1888 0 3 3 yes
## 1889 0 0 0 no
## 1890 0 1 0 no
## 1891 1 1 0 no
## 1893 0 1 0 no
## 1894 1 1 0 yes
## 1895 0 1 0 no
## 1896 0 1 0 no
## 1897 1 1 2 no
## 1899 0 1 1 no
## 1900 0 1 0 yes
## 1901 1 0 0 no
## 1902 1 0 0 no
## 1903 1 0 0 no
## 1905 0 0 0 no
## 1906 1 1 1 yes
## 1907 1 1 2 no
## 1909 0 0 0 no
## 1910 1 1 0 no
## 1911 0 1 0 no
## 1912 0 1 1 yes
## 1914 0 2 0 no
## 1915 0 0 0 no
## 1917 0 2 0 no
## 1918 0 1 2 yes
## 1919 0 1 0 no
## 1920 0 1 0 yes
## 1921 0 1 2 no
## 1922 1 0 0 no
## 1924 0 1 2 no
## 1925 1 0 0 no
## 1926 0 0 0 yes
## 1927 0 3 1 no
## 1928 0 1 0 yes
## 1929 1 1 1 no
## 1930 0 2 0 no
## 1931 0 1 0 no
## 1932 0 1 3 no
## 1933 1 1 0 no
## 1934 0 1 0 yes
## 1935 0 0 0 no
## 1936 0 0 0 no
## 1937 1 0 0 no
## 1938 0 0 0 no
## 1939 0 1 0 yes
## 1940 0 0 2 no
## 1942 0 0 0 yes
## 1943 0 1 0 yes
## 1945 1 0 0 no
## 1947 1 1 3 no
## 1948 0 0 3 no
## 1950 0 1 0 no
## 1951 0 1 2 no
## 1952 1 1 0 no
## 1953 0 1 3 no
## 1955 0 1 3 no
## 1956 0 1 1 yes
## 1957 0 3 0 yes
## 1958 1 1 0 yes
## 1959 1 1 0 no
## 1960 0 1 0 yes
## 1961 0 2 1 yes
## 1962 0 1 0 no
## 1963 1 1 0 no
## 1964 0 1 2 yes
## 1965 0 1 0 no
## 1966 0 0 0 no
## 1967 0 1 0 no
## 1968 0 0 0 no
## 1969 1 0 1 no
## 1970 0 0 0 no
## 1971 0 1 0 no
## 1972 0 1 0 no
## 1973 1 1 3 no
## 1974 0 1 0 yes
## 1975 1 0 1 yes
## 1976 0 1 0 no
## 1977 1 0 0 no
## 1978 1 1 3 no
## 1979 1 0 0 no
## 1980 1 1 0 no
## 1982 0 2 1 yes
## 1983 0 1 0 no
## 1987 1 0 2 no
## 1989 0 3 1 yes
## 1990 0 1 0 yes
## 1991 1 1 0 no
## 1992 0 0 0 yes
## 1993 0 2 0 no
## 1994 1 1 2 yes
## 1996 0 0 0 no
## 1997 0 1 0 yes
## 1998 0 1 0 no
## 1999 0 1 2 no
## 2002 1 1 0 no
## 2003 0 3 1 no
## 2004 0 1 2 no
## 2005 1 1 0 no
## 2006 0 1 3 no
## 2007 0 0 0 no
## 2008 0 1 0 no
## 2009 0 1 0 no
## 2010 0 1 3 no
## 2011 0 1 3 no
## 2012 0 1 0 no
## 2013 1 1 3 no
## 2014 0 2 0 yes
## 2015 0 1 0 no
## 2016 0 1 0 no
## 2017 0 1 1 no
## 2019 0 1 0 no
## 2020 0 3 2 no
## 2021 1 1 0 yes
## 2022 0 1 2 no
## 2023 0 1 1 no
## 2024 0 0 2 no
## 2025 0 3 0 yes
## 2026 1 1 0 no
## 2027 0 1 1 no
## 2028 1 1 2 no
## 2029 0 1 3 no
## 2030 0 2 1 yes
## 2031 0 1 0 yes
## 2032 1 1 0 no
## 2033 1 1 0 no
## 2034 1 1 0 no
## 2035 0 0 0 no
## 2036 1 2 1 no
## 2037 0 0 0 no
## 2038 0 0 0 yes
## 2039 1 1 2 no
## 2040 0 1 0 no
## 2041 0 1 0 no
## 2042 0 1 0 no
## 2043 0 2 2 no
## 2044 0 1 2 no
## 2045 0 1 0 no
## 2046 0 1 0 no
## 2047 0 2 0 no
## 2048 0 1 3 no
## 2049 0 1 0 no
## 2050 0 1 3 no
## 2051 0 1 0 no
## 2053 0 1 1 no
## 2055 1 1 2 no
## 2056 0 1 0 no
## 2057 0 1 0 no
## 2058 0 1 0 no
## 2059 0 0 0 no
## 2060 0 0 0 no
## 2061 0 1 0 no
## 2063 0 1 3 no
## 2064 1 0 0 no
## 2065 1 1 0 no
## 2066 1 0 3 no
## 2067 0 0 0 no
## 2068 0 1 0 no
## 2069 0 1 2 no
## 2070 0 0 0 no
## 2071 0 0 0 no
## 2072 0 1 2 no
## 2073 0 0 0 no
## [ reached 'max' / getOption("max.print") -- omitted 21191 rows ]Not needed since we are using random forest to train the model. And random forest is sensitive to feature scaling (but can perform normalization).
#evaluation method (repeatedcv)
set.seed(42)
#splitting the data 80% training and 20% testing
trainIndex <- createDataPartition(new_data2$h1n1_vaccine, p = 0.8, list = FALSE)
trainData <- new_data2[trainIndex, ]
testData <- new_data2[-trainIndex, ]#ensuring reproducibility
set.seed(123)
#applying training algorithms
lg_model <- glm(h1n1_vaccine~., data = trainData, family = binomial)summary(lg_model)##
## Call:
## glm(formula = h1n1_vaccine ~ ., family = binomial, data = trainData)
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -5.8597978 0.2081652 -28.150
## h1n1_concern -0.0851844 0.0292224 -2.915
## h1n1_knowledge 0.1410445 0.0389592 3.620
## behavioral_antiviral_meds 0.2311632 0.0947038 2.441
## behavioral_avoidance -0.0455093 0.0548163 -0.830
## behavioral_face_mask 0.1438480 0.0817932 1.759
## behavioral_wash_hands -0.0432500 0.0678791 -0.637
## behavioral_large_gatherings -0.2176573 0.0561479 -3.877
## behavioral_outside_home -0.0232810 0.0568675 -0.409
## behavioral_touch_face 0.0106934 0.0527749 0.203
## doctor_recc_h1n1 1.9783163 0.0616410 32.094
## doctor_recc_seasonal -0.5506528 0.0609376 -9.036
## chronic_med_condition 0.1381402 0.0481277 2.870
## child_under_6_months 0.3006558 0.0721726 4.166
## health_worker 0.8407939 0.0626135 13.428
## opinion_h1n1_vacc_effective 0.6659131 0.0300670 22.148
## opinion_h1n1_risk 0.3853314 0.0201927 19.083
## opinion_h1n1_sick_from_vacc -0.0039885 0.0189733 -0.210
## opinion_seas_vacc_effective 0.0850956 0.0269356 3.159
## opinion_seas_risk 0.1596139 0.0195194 8.177
## opinion_seas_sick_from_vacc -0.0729428 0.0187576 -3.889
## `age_group.18 - 34 Years` -0.4622963 0.0826621 -5.593
## `age_group.35 - 44 Years` -0.5213569 0.0900479 -5.790
## `age_group.45 - 54 Years` -0.4218345 0.0763876 -5.522
## `age_group.55 - 64 Years` -0.0950131 0.0665287 -1.428
## `age_group.65+ Years` NA NA NA
## education. 0.3100258 0.2500841 1.240
## `education.< 12 Years` -0.2401206 0.0925211 -2.595
## `education.12 Years` -0.0641866 0.0635856 -1.009
## `education.College Graduate` 0.1214238 0.0548541 2.214
## `education.Some College` NA NA NA
## race.Black -0.3684633 0.0944458 -3.901
## race.Hispanic -0.1506636 0.0936963 -1.608
## `race.Other or Multiple` 0.1830088 0.0913463 2.003
## race.White NA NA NA
## sex.Female -0.1542016 0.0458720 -3.362
## sex.Male NA NA NA
## income_poverty. 0.0972418 0.1030477 0.944
## `income_poverty.<= $75,000, Above Poverty` -0.0374394 0.0829707 -0.451
## `income_poverty.> $75,000` 0.0360077 0.0950474 0.379
## `income_poverty.Below Poverty` NA NA NA
## marital_status. -0.3028561 0.2749172 -1.102
## marital_status.Married 0.1232264 0.0522205 2.360
## `marital_status.Not Married` NA NA NA
## rent_or_own. 0.1842787 0.1488243 1.238
## rent_or_own.Own 0.0491395 0.0596367 0.824
## rent_or_own.Rent NA NA NA
## employment_status. -0.0516821 0.2588633 -0.200
## employment_status.Employed 0.0003749 0.1023691 0.004
## `employment_status.Not in Labor Force` 0.0370201 0.1048932 0.353
## employment_status.Unemployed NA NA NA
## `census_msa.MSA, Not Principle City` -0.0610905 0.0526794 -1.160
## `census_msa.MSA, Principle City` -0.0087674 0.0581537 -0.151
## `census_msa.Non-MSA` NA NA NA
## household_adults -0.0029686 0.0329135 -0.090
## household_children -0.0246466 0.0285242 -0.864
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## h1n1_concern 0.003556 **
## h1n1_knowledge 0.000294 ***
## behavioral_antiviral_meds 0.014650 *
## behavioral_avoidance 0.406417
## behavioral_face_mask 0.078632 .
## behavioral_wash_hands 0.524020
## behavioral_large_gatherings 0.000106 ***
## behavioral_outside_home 0.682254
## behavioral_touch_face 0.839430
## doctor_recc_h1n1 < 2e-16 ***
## doctor_recc_seasonal < 2e-16 ***
## chronic_med_condition 0.004101 **
## child_under_6_months 3.10e-05 ***
## health_worker < 2e-16 ***
## opinion_h1n1_vacc_effective < 2e-16 ***
## opinion_h1n1_risk < 2e-16 ***
## opinion_h1n1_sick_from_vacc 0.833499
## opinion_seas_vacc_effective 0.001582 **
## opinion_seas_risk 2.91e-16 ***
## opinion_seas_sick_from_vacc 0.000101 ***
## `age_group.18 - 34 Years` 2.24e-08 ***
## `age_group.35 - 44 Years` 7.05e-09 ***
## `age_group.45 - 54 Years` 3.35e-08 ***
## `age_group.55 - 64 Years` 0.153248
## `age_group.65+ Years` NA
## education. 0.215092
## `education.< 12 Years` 0.009451 **
## `education.12 Years` 0.312758
## `education.College Graduate` 0.026858 *
## `education.Some College` NA
## race.Black 9.57e-05 ***
## race.Hispanic 0.107835
## `race.Other or Multiple` 0.045128 *
## race.White NA
## sex.Female 0.000775 ***
## sex.Male NA
## income_poverty. 0.345344
## `income_poverty.<= $75,000, Above Poverty` 0.651819
## `income_poverty.> $75,000` 0.704807
## `income_poverty.Below Poverty` NA
## marital_status. 0.270624
## marital_status.Married 0.018288 *
## `marital_status.Not Married` NA
## rent_or_own. 0.215631
## rent_or_own.Own 0.409951
## rent_or_own.Rent NA
## employment_status. 0.841754
## employment_status.Employed 0.997078
## `employment_status.Not in Labor Force` 0.724140
## employment_status.Unemployed NA
## `census_msa.MSA, Not Principle City` 0.246185
## `census_msa.MSA, Principle City` 0.880163
## `census_msa.Non-MSA` NA
## household_adults 0.928132
## household_children 0.387556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 19630 on 18380 degrees of freedom
## Residual deviance: 14297 on 18334 degrees of freedom
## AIC: 14391
##
## Number of Fisher Scoring iterations: 5#Predict on test data
validations <- predict(lg_model, testData, type = "response")
#validations# Convert probabilities to binary predictions
validated_labels <- ifelse(validations > 0.5, "yes", "no")
# Print results
#print(validated_labels)# Ensure both predicted_labels and testData$h1n1_vaccine are factors with the same levels
validated_labels <- factor(validated_labels, levels = c("no", "yes"))
testData$h1n1_vaccine <- factor(testData$h1n1_vaccine, levels = c("no", "yes"))
# Now, apply confusionMatrix
confusionMatrix(validated_labels, testData$h1n1_vaccine)## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 3355 595
## yes 203 442
##
## Accuracy : 0.8263
## 95% CI : (0.8151, 0.8372)
## No Information Rate : 0.7743
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4263
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9429
## Specificity : 0.4262
## Pos Pred Value : 0.8494
## Neg Pred Value : 0.6853
## Prevalence : 0.7743
## Detection Rate : 0.7301
## Detection Prevalence : 0.8596
## Balanced Accuracy : 0.6846
##
## 'Positive' Class : no
## The accuracy of the dataset is quite okay at 0.8263
#adding "seasonal_vaccine class" back to original dataset
new_data3 <- cbind(new_data, h1n1[33])new_data3## h1n1_concern h1n1_knowledge behavioral_antiviral_meds behavioral_avoidance
## 0 1 0 0 0
## 1 3 2 0 1
## 3 1 1 0 1
## 4 2 1 0 1
## 5 3 1 0 1
## 6 0 0 0 0
## 7 1 0 0 1
## 8 0 2 0 1
## 9 2 1 0 1
## 10 2 1 0 1
## 11 1 2 0 1
## 12 1 1 0 1
## 13 1 1 0 1
## 14 3 1 0 1
## 15 1 1 0 1
## 16 2 1 0 1
## 17 1 1 0 0
## 18 1 2 0 1
## 19 2 2 0 0
## 20 3 1 0 1
## 21 2 2 0 1
## 22 1 1 0 1
## 23 3 0 1 0
## 24 0 1 0 1
## 25 1 2 0 1
## 26 3 1 0 0
## 27 3 2 0 1
## 28 0 1 0 0
## 29 2 1 0 0
## 30 3 2 0 1
## 31 2 1 0 1
## 32 2 2 0 1
## 33 2 1 0 1
## 34 1 1 0 1
## 35 2 1 1 1
## 36 2 2 0 1
## 37 1 1 0 0
## 38 1 1 0 1
## 39 2 1 0 1
## 40 0 2 0 0
## 41 2 1 0 1
## 43 2 1 1 1
## 45 2 1 1 1
## 46 1 2 0 0
## 47 0 2 0 0
## 48 3 2 0 1
## 49 1 1 0 0
## 50 2 2 0 1
## 51 0 1 0 1
## 52 1 1 0 1
## 53 1 2 0 0
## 54 1 1 0 1
## 55 1 1 0 1
## 56 2 1 0 1
## 58 2 2 0 1
## 59 3 1 0 1
## 60 3 2 0 1
## 61 3 2 0 1
## 62 2 1 0 1
## 63 2 1 0 1
## 65 1 1 0 0
## 66 2 1 0 0
## 67 2 2 0 0
## 68 1 1 0 0
## 69 1 2 0 1
## 70 2 1 1 1
## 71 3 1 0 0
## 72 1 1 0 0
## 73 3 2 0 1
## 74 2 1 0 0
## 75 3 1 0 1
## 77 1 1 0 1
## 78 0 1 0 0
## 79 3 1 0 1
## 80 1 1 0 1
## 81 3 1 0 1
## 82 2 2 0 1
## 83 3 1 0 1
## 84 2 2 0 0
## 85 2 1 0 1
## 86 0 2 0 0
## 87 3 1 0 1
## 88 1 0 0 1
## 91 2 2 1 1
## 92 1 1 0 0
## 93 2 1 0 0
## 95 2 2 0 1
## 98 0 1 0 0
## 99 2 0 1 1
## 100 2 1 0 1
## 101 1 2 0 1
## 103 2 1 0 1
## 104 2 0 0 1
## 106 0 2 0 1
## 107 2 1 0 1
## 108 3 2 0 1
## 109 0 1 1 1
## 110 2 2 0 1
## 111 2 1 0 1
## 112 3 2 0 1
## 113 2 1 0 1
## 114 1 1 0 1
## 115 3 0 0 1
## 116 2 1 0 0
## 117 2 1 0 1
## 118 0 2 0 1
## 120 1 1 0 0
## 121 1 1 0 0
## 122 2 2 0 1
## 123 3 2 0 1
## 124 0 1 0 0
## 125 2 1 0 1
## 126 0 1 0 1
## 127 0 2 0 1
## 128 3 1 0 1
## 129 2 1 0 1
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## opinion_h1n1_sick_from_vacc opinion_seas_vacc_effective opinion_seas_risk
## 0 2 2 1
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## income_poverty.Below Poverty marital_status. marital_status.Married
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## [ reached 'max' / getOption("max.print") -- omitted 21191 rows ]#converting 0 and 1 to "yes" and "no" for the decision class
new_data3$seasonal_vaccine <- factor(new_data3$seasonal_vaccine, levels = c(0, 1), labels = c("no", "yes"))
new_data3## h1n1_concern h1n1_knowledge behavioral_antiviral_meds behavioral_avoidance
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## 1930 0 4 1
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## 1934 1 5 4
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## 1936 0 1 1
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## 1948 0 5 5
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## 1970 0 4 1
## 1971 0 2 2
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## 1973 0 4 1
## 1974 0 5 5
## 1975 0 5 1
## 1976 0 2 1
## 1977 0 4 2
## 1978 0 4 2
## 1979 0 1 2
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## 1982 1 5 4
## 1983 0 3 4
## 1987 0 5 1
## 1989 0 5 4
## 1990 1 5 5
## 1991 0 2 4
## 1992 0 5 5
## 1993 0 4 2
## 1994 1 4 4
## 1996 0 3 1
## 1997 0 5 4
## 1998 1 5 2
## 1999 0 3 2
## 2002 0 4 2
## 2003 0 4 1
## 2004 0 4 1
## 2005 0 4 1
## 2006 0 4 2
## 2007 0 4 4
## 2008 0 5 2
## 2009 1 3 1
## 2010 0 1 1
## 2011 0 4 2
## 2012 0 5 2
## 2013 0 2 1
## 2014 0 5 4
## 2015 0 4 2
## 2016 0 4 2
## 2017 0 5 5
## 2019 0 4 2
## 2020 0 3 2
## 2021 0 5 2
## 2022 0 5 5
## 2023 0 4 2
## 2024 0 4 1
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## 2073 0 4 1
## opinion_h1n1_sick_from_vacc opinion_seas_vacc_effective opinion_seas_risk
## 0 2 2 1
## 1 4 4 2
## 3 5 5 4
## 4 2 3 1
## 5 1 5 4
## 6 1 4 2
## 7 1 4 2
## 8 1 4 2
## 9 2 4 2
## 10 2 5 4
## 11 2 4 2
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## 15 2 2 1
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## 18 2 5 4
## 19 1 5 2
## 20 2 4 2
## 21 1 3 3
## 22 2 4 1
## 23 4 4 4
## 24 2 1 2
## 25 2 2 4
## 26 4 5 5
## 27 2 2 2
## 28 1 2 1
## 29 5 4 1
## 30 2 4 2
## 31 4 4 1
## 32 2 4 2
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## 34 2 4 2
## 35 5 5 2
## 36 1 4 2
## 37 1 4 1
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## 43 4 1 4
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## 53 2 4 2
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## 61 5 3 5
## 62 2 5 1
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## 65 1 3 1
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## 68 2 4 4
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## 74 4 5 5
## 75 4 5 4
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## 83 4 4 4
## 84 1 5 4
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## 95 4 5 2
## 98 5 2 2
## 99 2 5 1
## 100 2 2 3
## 101 1 1 5
## 103 1 4 1
## 104 1 2 3
## 106 1 4 1
## 107 1 5 4
## 108 1 2 2
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## 211 4 1 1
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## 213 1 4 4
## 214 2 4 4
## 215 4 5 4
## 216 4 5 2
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## 219 4 5 4
## 220 2 5 4
## 221 2 5 1
## 222 2 2 2
## 223 2 5 2
## 224 2 5 4
## 225 4 4 1
## 226 1 4 2
## 227 4 5 2
## 228 2 5 2
## 229 1 5 4
## 230 4 5 2
## 231 4 5 4
## 232 4 2 1
## 233 1 4 2
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## 1978 1 0
## 1979 1 0
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## 1983 1 0
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## 1989 0 1
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## 1992 1 0
## 1993 0 1
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## 1996 0 0
## 1997 0 1
## 1998 0 1
## 1999 0 1
## 2002 0 0
## 2003 0 0
## 2004 0 1
## 2005 1 0
## 2006 0 1
## 2007 0 1
## 2008 1 0
## 2009 1 0
## 2010 0 1
## 2011 0 1
## 2012 0 1
## 2013 1 0
## 2014 0 0
## 2015 0 1
## 2016 1 0
## 2017 1 0
## 2019 1 0
## 2020 1 0
## 2021 1 0
## 2022 1 0
## 2023 0 1
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## 2030 1 0
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## 2073 1 0
## income_poverty.Below Poverty marital_status. marital_status.Married
## 0 1 0 0
## 1 1 0 0
## 3 1 0 0
## 4 0 0 1
## 5 0 0 1
## 6 0 0 0
## 7 0 0 1
## 8 0 0 1
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## 13 0 0 1
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## 15 0 0 1
## 16 0 0 1
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## 21 0 0 0
## 22 0 0 1
## 23 1 0 0
## 24 0 0 0
## 25 0 0 1
## 26 0 0 1
## 27 0 0 1
## 28 0 0 1
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 1
## 33 0 0 0
## 34 0 0 1
## 35 0 0 0
## 36 0 0 1
## 37 0 0 0
## 38 0 0 1
## 39 0 0 0
## 40 0 0 0
## 41 0 0 1
## 43 0 0 0
## 45 0 0 1
## 46 0 0 1
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## 49 0 0 1
## 50 0 0 1
## 51 0 0 0
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## 58 0 0 1
## 59 1 0 1
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## 61 0 0 1
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## 63 0 0 0
## 65 0 0 0
## 66 1 0 0
## 67 0 0 0
## 68 0 1 0
## 69 0 0 0
## 70 0 0 1
## 71 1 0 0
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## 75 0 1 0
## 77 0 0 1
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## 79 0 0 1
## 80 0 0 0
## 81 0 0 1
## 82 0 0 1
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## 88 0 0 0
## 91 0 0 1
## 92 0 0 0
## 93 0 0 1
## 95 0 0 0
## 98 1 0 0
## 99 1 0 1
## 100 0 0 0
## 101 0 0 1
## 103 1 0 1
## 104 0 0 0
## 106 0 0 0
## 107 0 0 1
## 108 0 0 1
## 109 1 0 1
## 110 0 0 1
## 111 0 0 1
## 112 0 0 0
## 113 0 0 0
## 114 0 0 1
## 115 0 0 0
## 116 0 0 1
## 117 1 0 1
## 118 0 0 1
## 120 0 0 1
## 121 0 0 0
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## 1914 1 0
## 1915 0 1
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## 1948 1 0
## 1950 0 1
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## 1953 0 1
## 1955 1 0
## 1956 0 1
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## 1958 0 0
## 1959 0 0
## 1960 1 0
## 1961 1 0
## 1962 1 0
## 1963 0 0
## 1964 1 0
## 1965 1 0
## 1966 1 0
## 1967 1 0
## 1968 0 1
## 1969 0 0
## 1970 1 0
## 1971 1 0
## 1972 1 0
## 1973 0 0
## 1974 0 1
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## 1976 0 1
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## 1978 0 0
## 1979 0 0
## 1980 0 0
## 1982 1 0
## 1983 0 1
## 1987 0 0
## 1989 1 0
## 1990 1 0
## 1991 0 0
## 1992 0 1
## 1993 0 1
## 1994 0 0
## 1996 1 0
## 1997 1 0
## 1998 1 0
## 1999 0 1
## 2002 0 0
## 2003 0 1
## 2004 1 0
## 2005 0 0
## 2006 1 0
## 2007 1 0
## 2008 1 0
## 2009 1 0
## 2010 0 1
## 2011 1 0
## 2012 0 1
## 2013 0 0
## 2014 1 0
## 2015 1 0
## 2016 1 0
## 2017 1 0
## 2019 0 1
## 2020 1 0
## 2021 0 0
## 2022 1 0
## 2023 1 0
## 2024 0 1
## 2025 1 0
## 2026 0 0
## 2027 0 1
## 2028 0 0
## 2029 1 0
## 2030 1 0
## 2031 0 1
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## 2035 0 1
## 2036 0 0
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## 2040 1 0
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## 2044 1 0
## 2045 1 0
## 2046 0 1
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## 2048 0 1
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## 2051 1 0
## 2053 1 0
## 2055 0 0
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## 2063 1 0
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## 2068 1 0
## 2069 1 0
## 2070 0 1
## 2071 0 1
## 2072 0 1
## 2073 0 1
## census_msa.Non-MSA household_adults household_children seasonal_vaccine
## 0 1 0 0 no
## 1 0 0 0 yes
## 3 0 0 0 yes
## 4 0 1 0 no
## 5 0 2 3 no
## 6 0 0 0 no
## 7 1 2 0 yes
## 8 0 1 0 no
## 9 0 0 0 no
## 10 0 2 0 yes
## 11 1 1 2 yes
## 12 0 1 0 yes
## 13 0 1 2 no
## 14 0 1 0 no
## 15 0 1 3 no
## 16 0 0 0 yes
## 17 0 1 0 no
## 18 1 2 0 no
## 19 0 1 0 no
## 20 0 0 0 no
## 21 0 1 0 yes
## 22 0 2 1 no
## 23 1 1 2 no
## 24 1 2 0 no
## 25 0 1 1 no
## 26 0 1 2 yes
## 27 1 1 0 no
## 28 1 1 0 no
## 29 0 0 0 no
## 30 0 0 2 no
## 31 0 3 0 no
## 32 0 1 2 yes
## 33 0 0 0 no
## 34 0 1 2 yes
## 35 0 0 0 yes
## 36 1 2 0 yes
## 37 0 0 0 no
## 38 0 1 0 yes
## 39 0 0 0 yes
## 40 0 0 0 no
## 41 0 1 0 yes
## 43 0 0 1 yes
## 45 0 2 1 no
## 46 0 1 0 yes
## 47 0 1 0 no
## 48 1 1 0 yes
## 49 0 1 3 yes
## 50 1 1 2 no
## 51 0 0 0 yes
## 52 1 1 0 no
## 53 0 0 0 yes
## 54 0 0 1 no
## 55 0 1 0 no
## 56 1 0 0 yes
## 58 0 1 2 no
## 59 0 1 2 yes
## 60 0 0 0 yes
## 61 0 1 2 yes
## 62 0 2 0 no
## 63 0 3 1 yes
## 65 0 0 0 no
## 66 1 1 2 no
## 67 0 0 1 yes
## 68 1 1 0 yes
## 69 0 0 0 no
## 70 0 2 1 no
## 71 0 0 0 yes
## 72 0 0 0 yes
## 73 0 0 0 yes
## 74 1 1 0 yes
## 75 0 1 2 yes
## 77 0 1 2 yes
## 78 0 0 0 yes
## 79 1 1 0 yes
## 80 1 0 0 no
## 81 0 1 0 yes
## 82 0 1 1 yes
## 83 0 0 1 yes
## 84 1 1 0 yes
## 85 0 1 0 yes
## 86 0 2 1 no
## 87 0 1 1 no
## 88 1 0 0 yes
## 91 1 1 2 yes
## 92 0 0 0 no
## 93 0 1 2 yes
## 95 0 2 0 no
## 98 0 3 3 no
## 99 0 1 0 yes
## 100 0 1 0 no
## 101 1 1 0 yes
## 103 1 1 1 no
## 104 0 0 0 no
## 106 0 2 0 no
## 107 1 1 2 yes
## 108 0 1 2 no
## 109 0 3 3 no
## 110 1 1 0 yes
## 111 0 1 0 no
## 112 0 2 0 yes
## 113 0 2 0 no
## 114 0 1 2 yes
## 115 0 0 0 no
## 116 1 1 0 yes
## 117 0 1 0 no
## 118 0 1 0 yes
## 120 1 1 0 no
## 121 0 0 0 no
## 122 0 0 0 yes
## 123 0 1 0 yes
## 124 0 1 0 no
## 125 0 1 3 yes
## 126 0 1 0 no
## 127 0 3 0 yes
## 128 1 0 0 yes
## 129 0 0 0 no
## 130 1 1 0 no
## 131 0 0 0 yes
## 132 0 1 2 no
## 133 0 0 0 yes
## 134 0 0 0 yes
## 135 0 0 0 yes
## 137 0 1 0 no
## 138 0 1 0 yes
## 139 0 1 3 no
## 140 0 1 0 yes
## 141 1 1 0 no
## 142 0 0 0 no
## 143 0 0 0 no
## 144 1 0 1 no
## 145 0 0 0 no
## 146 0 2 2 yes
## 147 0 0 0 yes
## 148 1 1 0 yes
## 151 0 0 0 yes
## 152 1 1 1 yes
## 154 1 0 0 no
## 155 0 1 2 yes
## 156 0 1 0 no
## 157 1 0 0 no
## 158 1 2 0 yes
## 159 0 1 0 yes
## 160 1 1 0 yes
## 161 0 1 2 no
## 162 0 1 3 no
## 163 0 1 3 no
## 164 0 0 0 yes
## 165 0 1 2 no
## 166 1 1 2 no
## 167 0 1 0 yes
## 168 1 1 0 yes
## 170 1 2 3 no
## 171 1 0 3 yes
## 172 0 1 0 yes
## 173 1 2 0 no
## 174 0 3 1 no
## 176 1 0 0 no
## 177 0 0 0 yes
## 178 0 1 1 no
## 179 0 1 3 no
## 181 0 1 0 no
## 182 0 1 0 no
## 185 1 1 0 yes
## 186 0 0 0 no
## 187 0 0 0 no
## 188 1 2 0 no
## 189 0 1 0 yes
## 190 1 0 3 no
## 191 0 1 1 no
## 192 0 3 0 yes
## 193 0 1 0 yes
## 194 1 1 0 no
## 195 1 1 0 yes
## 196 0 1 0 yes
## 197 1 2 2 no
## 199 0 2 0 yes
## 200 0 1 1 no
## 201 1 1 0 no
## 202 0 1 0 no
## 204 0 3 0 yes
## 206 1 1 3 no
## 207 0 0 0 yes
## 208 0 1 0 yes
## 209 1 1 0 yes
## 210 0 0 2 no
## 211 0 1 3 yes
## 212 0 1 0 yes
## 213 0 2 0 yes
## 214 1 1 0 no
## 215 0 1 0 yes
## 216 0 1 0 yes
## 218 0 0 0 yes
## 219 1 1 2 yes
## 220 0 1 0 no
## 221 0 1 0 no
## 222 0 1 0 no
## 223 0 1 0 yes
## 224 0 1 0 yes
## 225 0 1 0 no
## 226 0 0 0 yes
## 227 0 2 0 yes
## 228 0 1 1 yes
## 229 0 1 0 yes
## 230 0 1 0 yes
## 231 1 1 0 yes
## 232 1 0 0 yes
## 233 0 1 1 no
## 235 0 1 0 yes
## 236 1 1 0 yes
## 240 0 0 0 no
## 241 0 1 0 yes
## 242 1 0 3 no
## 243 1 0 0 yes
## 244 0 3 0 yes
## 245 0 0 0 no
## 246 0 1 3 no
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## 1363 1 2 3 no
## 1364 0 1 0 yes
## 1365 0 2 2 no
## 1366 0 2 1 no
## 1367 0 1 0 no
## 1369 1 2 1 no
## 1371 0 2 0 no
## 1372 0 0 0 no
## 1373 0 0 0 no
## 1374 0 0 0 no
## 1375 1 1 0 no
## 1376 0 0 0 yes
## 1377 0 1 0 yes
## 1378 1 2 0 no
## 1379 1 1 0 no
## 1380 0 1 0 yes
## 1381 1 0 0 no
## 1382 1 1 0 no
## 1383 0 0 0 no
## 1385 1 1 3 yes
## 1386 0 1 0 yes
## 1387 1 1 3 yes
## 1389 1 1 0 no
## 1390 0 1 0 yes
## 1391 0 1 2 yes
## 1392 0 2 0 yes
## 1393 0 0 0 no
## 1394 0 3 2 no
## 1395 1 1 0 yes
## 1396 0 0 0 yes
## 1397 1 0 0 yes
## 1398 0 0 0 no
## 1399 0 1 0 yes
## 1401 0 0 0 yes
## 1402 0 2 1 yes
## 1403 0 0 0 yes
## 1404 0 0 0 yes
## 1405 0 0 0 yes
## 1406 0 0 1 yes
## 1407 0 3 1 no
## 1408 1 1 0 yes
## 1409 0 1 0 yes
## 1410 0 0 0 yes
## 1411 0 3 1 no
## 1412 0 0 0 yes
## 1414 0 1 0 no
## 1415 0 1 0 yes
## 1416 0 0 0 yes
## 1417 1 1 0 no
## 1418 0 1 0 no
## 1419 0 1 1 no
## 1420 0 1 0 yes
## 1422 0 0 0 no
## 1423 0 1 0 yes
## 1425 0 1 3 yes
## 1426 1 1 0 yes
## 1427 1 1 0 no
## 1428 1 0 0 no
## 1429 0 1 0 no
## 1430 0 1 0 yes
## 1431 0 1 1 yes
## 1432 1 0 0 yes
## 1433 0 1 0 no
## 1434 0 1 0 no
## 1435 0 1 3 no
## 1437 0 1 1 yes
## 1438 0 0 0 yes
## 1439 1 0 1 no
## 1440 0 1 3 yes
## 1441 0 1 0 yes
## 1443 0 1 1 yes
## 1444 0 1 2 no
## 1445 0 1 0 yes
## 1446 0 0 0 no
## 1447 0 0 0 yes
## 1448 0 1 0 no
## 1449 0 1 1 no
## 1450 0 1 0 yes
## 1452 0 0 0 yes
## 1453 0 1 0 yes
## 1454 1 1 1 yes
## 1455 0 1 1 no
## 1456 0 0 0 no
## 1457 1 1 1 no
## 1458 0 0 0 no
## 1459 0 2 2 no
## 1460 1 1 0 no
## 1461 0 1 3 no
## 1462 0 1 0 no
## 1464 0 0 0 no
## 1465 0 3 2 yes
## 1466 0 0 0 no
## 1467 0 1 2 yes
## 1468 0 0 1 no
## 1469 0 1 0 yes
## 1470 0 0 0 no
## 1472 0 1 0 no
## 1474 0 1 2 yes
## 1475 1 0 0 yes
## 1476 0 1 2 yes
## 1477 1 2 0 no
## 1478 0 1 3 no
## 1479 0 2 0 no
## 1480 0 1 0 yes
## 1481 0 1 2 no
## 1482 0 0 0 yes
## 1483 0 1 0 no
## 1485 1 1 2 no
## 1486 0 1 0 no
## 1487 0 1 0 no
## 1488 0 0 0 yes
## 1490 1 2 0 no
## 1491 0 1 0 yes
## 1492 1 1 0 no
## 1493 0 1 1 no
## 1494 0 0 0 yes
## 1495 0 0 1 yes
## 1496 0 1 0 yes
## 1498 0 0 0 no
## 1499 0 1 0 no
## 1500 1 1 2 no
## 1501 0 1 0 yes
## 1502 1 1 1 no
## 1504 0 2 0 no
## 1505 0 1 2 no
## 1508 0 1 0 yes
## 1509 1 1 0 no
## 1510 0 1 0 no
## 1511 0 2 0 no
## 1512 1 0 0 no
## 1513 1 1 1 no
## 1514 0 1 3 no
## 1515 0 1 0 no
## 1517 1 1 3 no
## 1518 0 0 0 yes
## 1519 0 1 2 yes
## 1520 0 3 1 no
## 1521 0 2 0 no
## 1522 1 0 1 no
## 1523 1 0 0 yes
## 1524 0 1 0 yes
## 1525 0 1 0 no
## 1526 0 2 3 yes
## 1527 1 0 0 yes
## 1528 0 1 0 no
## 1529 0 1 2 no
## 1530 0 0 0 no
## 1531 1 1 0 yes
## 1532 1 2 0 no
## 1533 1 0 0 yes
## 1534 1 1 0 no
## 1535 0 0 0 no
## 1536 0 1 1 no
## 1537 0 1 0 yes
## 1538 0 0 0 yes
## 1539 0 1 2 no
## 1540 0 1 0 no
## 1541 1 0 2 no
## 1542 0 1 0 yes
## 1544 1 0 0 no
## 1545 0 0 2 no
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## 1550 0 1 1 no
## 1551 0 1 0 yes
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## 1554 0 1 0 no
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## 1559 0 0 0 yes
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## 1570 0 0 2 yes
## 1571 0 1 3 no
## 1572 0 1 0 yes
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## 1575 0 2 0 no
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## 1579 1 1 1 yes
## 1580 0 0 0 no
## 1581 0 1 2 no
## 1582 0 0 0 yes
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## 1585 1 1 0 no
## 1586 1 1 3 no
## 1587 1 1 2 yes
## 1588 1 1 0 no
## 1589 0 1 2 yes
## 1590 1 0 0 yes
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## 1592 0 2 2 no
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## 1628 1 0 0 yes
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## 1633 1 1 0 yes
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## 1652 0 1 0 no
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## 1657 1 0 0 no
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## 1663 0 0 0 yes
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## 1665 0 1 1 no
## 1666 1 0 0 no
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## 1671 1 0 0 no
## 1673 0 0 0 no
## 1674 0 0 0 yes
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## 1676 0 0 0 yes
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## 1687 1 0 0 yes
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## 1712 1 0 0 no
## 1713 0 0 0 no
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## 1718 0 3 3 no
## 1720 0 1 1 yes
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## 1722 0 2 0 no
## 1723 0 1 1 no
## 1724 1 1 2 yes
## 1725 0 1 0 yes
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## 1730 0 1 0 no
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## 1740 0 0 0 yes
## 1741 1 1 0 yes
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## 1748 1 0 0 no
## 1749 1 1 0 no
## 1750 0 1 0 yes
## 1751 0 0 0 no
## 1752 1 1 2 no
## 1753 0 3 0 no
## 1755 0 1 0 no
## 1756 0 1 0 no
## 1757 0 1 0 yes
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## 1760 1 0 0 yes
## 1761 1 1 3 no
## 1763 1 1 0 yes
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## 1765 0 1 3 no
## 1766 0 3 3 no
## 1767 0 1 0 yes
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## 1769 0 1 2 no
## 1770 0 2 3 no
## 1771 0 1 2 no
## 1773 1 0 0 no
## 1776 0 1 0 no
## 1777 0 1 0 no
## 1778 0 0 0 yes
## 1779 0 1 3 no
## 1780 0 1 0 no
## 1781 1 0 0 no
## 1782 0 1 0 no
## 1783 0 2 2 yes
## 1784 0 2 0 yes
## 1785 1 1 2 yes
## 1786 0 1 1 yes
## 1787 0 1 0 yes
## 1788 0 0 2 no
## 1789 0 2 0 no
## 1790 0 0 0 yes
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## 1793 0 1 0 yes
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## 1804 1 0 0 yes
## 1805 1 2 2 no
## 1807 1 0 0 no
## 1808 1 0 2 yes
## 1809 0 0 0 no
## 1810 0 0 0 no
## 1811 1 1 3 no
## 1813 0 1 0 no
## 1814 0 0 2 yes
## 1815 0 1 0 no
## 1816 1 1 0 yes
## 1817 0 1 0 yes
## 1818 0 1 2 yes
## 1819 0 1 0 no
## 1820 0 2 0 no
## 1821 1 2 0 yes
## 1822 1 0 0 no
## 1824 1 1 0 no
## 1825 0 2 0 no
## 1826 0 1 1 no
## 1827 1 1 2 no
## 1828 1 3 1 yes
## 1829 1 1 0 yes
## 1830 1 1 0 no
## 1832 0 1 1 no
## 1833 0 0 0 no
## 1834 0 1 2 no
## 1835 0 0 0 no
## 1836 1 2 1 no
## 1837 0 1 2 no
## 1839 0 1 0 yes
## 1840 1 2 2 no
## 1841 1 0 0 yes
## 1842 0 1 0 yes
## 1844 0 0 0 yes
## 1845 0 0 0 yes
## 1846 0 1 0 yes
## 1848 0 0 0 yes
## 1849 0 2 0 yes
## 1850 0 1 0 no
## 1853 0 0 0 yes
## 1854 0 1 0 no
## 1855 0 1 1 no
## 1856 0 0 0 no
## 1857 1 2 0 no
## 1859 0 1 3 no
## 1861 0 0 0 yes
## 1863 0 1 1 no
## 1864 1 1 0 yes
## 1865 0 0 0 yes
## 1866 0 1 0 yes
## 1867 0 1 0 no
## 1868 1 1 0 yes
## 1869 0 1 0 yes
## 1870 1 0 0 no
## 1871 1 0 0 no
## 1872 0 0 1 no
## 1873 0 1 0 yes
## 1874 0 0 0 no
## 1875 0 1 0 no
## 1876 0 0 0 yes
## 1877 1 0 0 yes
## 1878 0 0 0 no
## 1879 0 1 0 yes
## 1880 0 0 0 yes
## 1881 0 1 1 yes
## 1882 1 1 0 no
## 1883 0 0 0 yes
## 1884 0 0 0 no
## 1885 0 2 0 yes
## 1886 1 1 0 yes
## 1887 0 0 0 no
## 1888 0 3 3 yes
## 1889 0 0 0 yes
## 1890 0 1 0 yes
## 1891 1 1 0 yes
## 1893 0 1 0 yes
## 1894 1 1 0 yes
## 1895 0 1 0 yes
## 1896 0 1 0 no
## 1897 1 1 2 no
## 1899 0 1 1 no
## 1900 0 1 0 yes
## 1901 1 0 0 yes
## 1902 1 0 0 no
## 1903 1 0 0 yes
## 1905 0 0 0 no
## 1906 1 1 1 yes
## 1907 1 1 2 no
## 1909 0 0 0 yes
## 1910 1 1 0 yes
## 1911 0 1 0 no
## 1912 0 1 1 no
## 1914 0 2 0 yes
## 1915 0 0 0 yes
## 1917 0 2 0 no
## 1918 0 1 2 no
## 1919 0 1 0 yes
## 1920 0 1 0 no
## 1921 0 1 2 no
## 1922 1 0 0 yes
## 1924 0 1 2 no
## 1925 1 0 0 yes
## 1926 0 0 0 yes
## 1927 0 3 1 no
## 1928 0 1 0 yes
## 1929 1 1 1 no
## 1930 0 2 0 no
## 1931 0 1 0 yes
## 1932 0 1 3 no
## 1933 1 1 0 yes
## 1934 0 1 0 yes
## 1935 0 0 0 yes
## 1936 0 0 0 yes
## 1937 1 0 0 no
## 1938 0 0 0 yes
## 1939 0 1 0 yes
## 1940 0 0 2 no
## 1942 0 0 0 yes
## 1943 0 1 0 no
## 1945 1 0 0 no
## 1947 1 1 3 yes
## 1948 0 0 3 no
## 1950 0 1 0 no
## 1951 0 1 2 yes
## 1952 1 1 0 yes
## 1953 0 1 3 no
## 1955 0 1 3 yes
## 1956 0 1 1 yes
## 1957 0 3 0 yes
## 1958 1 1 0 yes
## 1959 1 1 0 no
## 1960 0 1 0 yes
## 1961 0 2 1 no
## 1962 0 1 0 yes
## 1963 1 1 0 yes
## 1964 0 1 2 no
## 1965 0 1 0 no
## 1966 0 0 0 no
## 1967 0 1 0 no
## 1968 0 0 0 yes
## 1969 1 0 1 no
## 1970 0 0 0 yes
## 1971 0 1 0 yes
## 1972 0 1 0 no
## 1973 1 1 3 no
## 1974 0 1 0 no
## 1975 1 0 1 yes
## 1976 0 1 0 no
## 1977 1 0 0 no
## 1978 1 1 3 no
## 1979 1 0 0 no
## 1980 1 1 0 yes
## 1982 0 2 1 no
## 1983 0 1 0 yes
## 1987 1 0 2 no
## 1989 0 3 1 yes
## 1990 0 1 0 yes
## 1991 1 1 0 no
## 1992 0 0 0 yes
## 1993 0 2 0 no
## 1994 1 1 2 yes
## 1996 0 0 0 yes
## 1997 0 1 0 yes
## 1998 0 1 0 no
## 1999 0 1 2 yes
## 2002 1 1 0 yes
## 2003 0 3 1 no
## 2004 0 1 2 no
## 2005 1 1 0 no
## 2006 0 1 3 yes
## 2007 0 0 0 yes
## 2008 0 1 0 yes
## 2009 0 1 0 yes
## 2010 0 1 3 yes
## 2011 0 1 3 yes
## 2012 0 1 0 yes
## 2013 1 1 3 no
## 2014 0 2 0 yes
## 2015 0 1 0 yes
## 2016 0 1 0 yes
## 2017 0 1 1 yes
## 2019 0 1 0 no
## 2020 0 3 2 no
## 2021 1 1 0 yes
## 2022 0 1 2 no
## 2023 0 1 1 no
## 2024 0 0 2 no
## 2025 0 3 0 yes
## 2026 1 1 0 yes
## 2027 0 1 1 no
## 2028 1 1 2 no
## 2029 0 1 3 yes
## 2030 0 2 1 yes
## 2031 0 1 0 yes
## 2032 1 1 0 yes
## 2033 1 1 0 no
## 2034 1 1 0 no
## 2035 0 0 0 yes
## 2036 1 2 1 yes
## 2037 0 0 0 yes
## 2038 0 0 0 yes
## 2039 1 1 2 no
## 2040 0 1 0 no
## 2041 0 1 0 no
## 2042 0 1 0 yes
## 2043 0 2 2 yes
## 2044 0 1 2 no
## 2045 0 1 0 no
## 2046 0 1 0 no
## 2047 0 2 0 yes
## 2048 0 1 3 yes
## 2049 0 1 0 no
## 2050 0 1 3 no
## 2051 0 1 0 yes
## 2053 0 1 1 no
## 2055 1 1 2 yes
## 2056 0 1 0 no
## 2057 0 1 0 yes
## 2058 0 1 0 yes
## 2059 0 0 0 no
## 2060 0 0 0 yes
## 2061 0 1 0 yes
## 2063 0 1 3 no
## 2064 1 0 0 no
## 2065 1 1 0 no
## 2066 1 0 3 no
## 2067 0 0 0 yes
## 2068 0 1 0 yes
## 2069 0 1 2 no
## 2070 0 0 0 yes
## 2071 0 0 0 yes
## 2072 0 1 2 no
## 2073 0 0 0 no
## [ reached 'max' / getOption("max.print") -- omitted 21191 rows ]Not needed since we are using random forest to train the model. And random forest is sensitive to feature scaling (but can perform normalization).
#evaluation method (repeatedcv)
set.seed(42)
#splitting the data 80% training and 20% testing
trainIndex <- createDataPartition(new_data3$seasonal_vaccine, p = 0.8, list = FALSE)
trainData2 <- new_data3[trainIndex, ]
testData2 <- new_data3[-trainIndex, ]#ensuring reproducibility
set.seed(123)
#applying training algorithms
seasonal_lg_model <- glm(seasonal_vaccine~., data = trainData2, family = binomial)summary(seasonal_lg_model)##
## Call:
## glm(formula = seasonal_vaccine ~ ., family = binomial, data = trainData2)
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.32832 0.17198 -25.168 < 2e-16
## h1n1_concern 0.00528 0.02587 0.204 0.838280
## h1n1_knowledge 0.19988 0.03430 5.827 5.65e-09
## behavioral_antiviral_meds 0.11616 0.08863 1.311 0.189958
## behavioral_avoidance 0.01053 0.04792 0.220 0.826069
## behavioral_face_mask 0.04915 0.07918 0.621 0.534799
## behavioral_wash_hands 0.02988 0.05763 0.518 0.604140
## behavioral_large_gatherings -0.02948 0.05028 -0.586 0.557603
## behavioral_outside_home -0.02556 0.05116 -0.500 0.617346
## behavioral_touch_face 0.16016 0.04603 3.480 0.000502
## doctor_recc_h1n1 -0.28849 0.05971 -4.832 1.35e-06
## doctor_recc_seasonal 1.45381 0.05235 27.771 < 2e-16
## chronic_med_condition 0.21470 0.04436 4.840 1.30e-06
## child_under_6_months 0.07865 0.07004 1.123 0.261489
## health_worker 0.86094 0.06328 13.605 < 2e-16
## opinion_h1n1_vacc_effective 0.02632 0.02269 1.160 0.245940
## opinion_h1n1_risk 0.02798 0.01922 1.456 0.145517
## opinion_h1n1_sick_from_vacc -0.04299 0.01746 -2.462 0.013822
## opinion_seas_vacc_effective 0.57178 0.02337 24.467 < 2e-16
## opinion_seas_risk 0.57388 0.01769 32.447 < 2e-16
## opinion_seas_sick_from_vacc -0.19243 0.01710 -11.255 < 2e-16
## `age_group.18 - 34 Years` -1.53654 0.07359 -20.879 < 2e-16
## `age_group.35 - 44 Years` -1.30413 0.07890 -16.529 < 2e-16
## `age_group.45 - 54 Years` -1.13147 0.06753 -16.756 < 2e-16
## `age_group.55 - 64 Years` -0.82698 0.06068 -13.628 < 2e-16
## `age_group.65+ Years` NA NA NA NA
## education. 0.05198 0.21318 0.244 0.807350
## `education.< 12 Years` -0.28214 0.07869 -3.585 0.000336
## `education.12 Years` -0.06635 0.05497 -1.207 0.227371
## `education.College Graduate` 0.12516 0.04889 2.560 0.010463
## `education.Some College` NA NA NA NA
## race.Black -0.28421 0.07615 -3.732 0.000190
## race.Hispanic -0.21313 0.08124 -2.623 0.008707
## `race.Other or Multiple` 0.10794 0.08137 1.327 0.184638
## race.White NA NA NA NA
## sex.Female -0.03152 0.04034 -0.782 0.434471
## sex.Male NA NA NA NA
## income_poverty. 0.27027 0.08945 3.021 0.002516
## `income_poverty.<= $75,000, Above Poverty` 0.15241 0.07281 2.093 0.036327
## `income_poverty.> $75,000` 0.32520 0.08374 3.883 0.000103
## `income_poverty.Below Poverty` NA NA NA NA
## marital_status. -0.02002 0.22546 -0.089 0.929229
## marital_status.Married 0.07785 0.04597 1.694 0.090351
## `marital_status.Not Married` NA NA NA NA
## rent_or_own. 0.26795 0.12756 2.101 0.035671
## rent_or_own.Own 0.17865 0.05241 3.409 0.000653
## rent_or_own.Rent NA NA NA NA
## employment_status. 0.25757 0.21778 1.183 0.236918
## employment_status.Employed 0.16958 0.08901 1.905 0.056750
## `employment_status.Not in Labor Force` 0.26723 0.09164 2.916 0.003544
## employment_status.Unemployed NA NA NA NA
## `census_msa.MSA, Not Principle City` 0.16000 0.04680 3.419 0.000628
## `census_msa.MSA, Principle City` 0.14792 0.05216 2.836 0.004569
## `census_msa.Non-MSA` NA NA NA NA
## household_adults -0.04367 0.02867 -1.523 0.127717
## household_children -0.05780 0.02501 -2.311 0.020838
##
## (Intercept) ***
## h1n1_concern
## h1n1_knowledge ***
## behavioral_antiviral_meds
## behavioral_avoidance
## behavioral_face_mask
## behavioral_wash_hands
## behavioral_large_gatherings
## behavioral_outside_home
## behavioral_touch_face ***
## doctor_recc_h1n1 ***
## doctor_recc_seasonal ***
## chronic_med_condition ***
## child_under_6_months
## health_worker ***
## opinion_h1n1_vacc_effective
## opinion_h1n1_risk
## opinion_h1n1_sick_from_vacc *
## opinion_seas_vacc_effective ***
## opinion_seas_risk ***
## opinion_seas_sick_from_vacc ***
## `age_group.18 - 34 Years` ***
## `age_group.35 - 44 Years` ***
## `age_group.45 - 54 Years` ***
## `age_group.55 - 64 Years` ***
## `age_group.65+ Years`
## education.
## `education.< 12 Years` ***
## `education.12 Years`
## `education.College Graduate` *
## `education.Some College`
## race.Black ***
## race.Hispanic **
## `race.Other or Multiple`
## race.White
## sex.Female
## sex.Male
## income_poverty. **
## `income_poverty.<= $75,000, Above Poverty` *
## `income_poverty.> $75,000` ***
## `income_poverty.Below Poverty`
## marital_status.
## marital_status.Married .
## `marital_status.Not Married`
## rent_or_own. *
## rent_or_own.Own ***
## rent_or_own.Rent
## employment_status.
## employment_status.Employed .
## `employment_status.Not in Labor Force` **
## employment_status.Unemployed
## `census_msa.MSA, Not Principle City` ***
## `census_msa.MSA, Principle City` **
## `census_msa.Non-MSA`
## household_adults
## household_children *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25446 on 18380 degrees of freedom
## Residual deviance: 17366 on 18334 degrees of freedom
## AIC: 17460
##
## Number of Fisher Scoring iterations: 5#Predict on test data
validations2 <- predict(seasonal_lg_model, testData2, type = "response")
#validations2# Convert probabilities to binary predictions
validated_labels2 <- ifelse(validations2 > 0.5, "yes", "no")
# Print results
#print(validated_labels2)# Ensure both predicted_labels and testData$h1n1_vaccine are factors with the same levels
validated_labels2 <- factor(validated_labels2, levels = c("no", "yes"))
testData2$seasonal_vaccine <- factor(testData2$seasonal_vaccine, levels = c("no", "yes"))
# Now, apply confusionMatrix
confusionMatrix(validated_labels2, testData2$seasonal_vaccine)## Confusion Matrix and Statistics
##
## Reference
## Prediction no yes
## no 1916 519
## yes 482 1678
##
## Accuracy : 0.7822
## 95% CI : (0.7699, 0.794)
## No Information Rate : 0.5219
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.5632
##
## Mcnemar's Test P-Value : 0.2552
##
## Sensitivity : 0.7990
## Specificity : 0.7638
## Pos Pred Value : 0.7869
## Neg Pred Value : 0.7769
## Prevalence : 0.5219
## Detection Rate : 0.4170
## Detection Prevalence : 0.5299
## Balanced Accuracy : 0.7814
##
## 'Positive' Class : no
## The accuracy of the dataset is quite okay at 0.7822